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This review provides a comprehensive overview of recent advances in cancer biomarker discovery through multi-omics data integration combined with artificial intelligence (AI), highlighting how these approaches facilitate early diagnosis, improve prognostic accuracy, and enable personalized treatment in precision oncology. We conducted a literature review of high-throughput technologies, including genomics, transcriptomics, proteomics, and metabolomics, with particular emphasis on integrative computational strategies, such as translational bioinformatics, AI, and emerging quantum machine learning (QML), that support multidimensional data analysis for biomarker identification. Integrative analyses of multi-omics datasets using advanced computational methods have expanded our understanding of tumor molecular heterogeneity and complexity. Novel approaches, including liquid biopsy, digital pathology, and artificial intelligence-assisted image analysis, enhance early detection, therapy monitoring, and individualized interventions. Despite these advances, data standardization, harmonization of heterogeneous datasets, reproducibility, and ethical considerations remain challenges that must be addressed to enable effective clinical translation. Nevertheless, the integration of multi-omics data with AI and advanced computational methods is transforming cancer biomarker discovery, bridging molecular complexity with actionable clinical insights, and improving early detection, precise patient stratification, and therapy monitoring. Future progress will depend on interdisciplinary collaboration, global data harmonization, and continued technological innovation. Collectively, these advances pave the way toward truly personalized, proactive cancer care and position oncology as a data-driven discipline with improved patient outcomes.
Günel et al. (Mon,) studied this question.